Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Iegultais uz gadījumu vērstais jauktās metodes dizains× | Diferencētā secīgā jauktās metodes dizains× | |
|---|---|---|
| Nozare | Pētījuma dizains | Pētījuma dizains |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2000s (formalized ~2007-2011) | 2007 (formalized in Creswell & Plano Clark's mixed methods typology) |
| Autors≠ | Creswell & Plano Clark (embedded design); Yin (case-study framework) | John W. Creswell & Vicki L. Plano Clark |
| Tips | Mixed methods research design | Mixed methods research design |
| Pirmavots | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 | Creswell, J. W., & Plano Clark, V. L. (2018). Designing and Conducting Mixed Methods Research (3rd ed.). Sage. ISBN: 978-1483344379 |
| Citi nosaukumi | embedded case-study mixed methods, case-centered embedded MMR, nested case mixed methods, embedded within-case mixed design | explanatory sequential design, QUAN → qual design, two-phase explanatory design, sequential explanatory design |
| Saistītās | 6 | 6 |
| Kopsavilkums≠ | Embedded case-focused mixed methods design combines a case-study unit of analysis with an embedded mixed methods structure, nesting one smaller data strand — typically qualitative — within a dominant primary strand — typically quantitative — all organized around one or more bounded cases. This design enables researchers to answer within-case questions at multiple levels, capturing both statistical patterns and rich contextual meaning for a specific case or set of cases. | The explanatory sequential mixed methods design is a two-phase research approach in which a quantitative study is conducted first, and qualitative data are then collected specifically to help explain or elaborate the initial quantitative results. The quantitative phase carries greater priority; the qualitative phase is purposefully built around the findings — such as surprising results, outliers, or statistically significant relationships — that need deeper interpretation. |
| ScholarGateDatu kopa ↗ |
|
|